Comparative Study of Forward and Backward Chaining in Artificial Intelligence
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Date
2016
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International Journal Of Engineering And Computer Science Volume 5 Issue 4 April 2016, Page No. 16239-16242
Abstract
An artificial intelligence system is capable of elucidating and representing knowledge along with storing and manipulating data.
Knowledge could be a collection of facts and principles build up by human. It is the refined form of information. Knowledge representation
is to represent knowledge in a manner that facilitates the power to draw conclusions from knowledge. Knowledge representation is a good
approach as conventional procedural code is not the best way to use for solving complex problems. Frames, Semantic Nets, Systems
Architecture, Rules, and Ontology are its techniques to represent knowledge. Forward and backward chaining are the two main methods of
reasoning used in an inference engine. It is a very common approach for “expert systems”, business and systems. This paper focus on the
concept of knowledge representation in artificial intelligence and the elaborating the comparison of forward and backward chaining.